多目标跟踪中基于SOT和重匹配的防遗漏机制
Anti-missing Mechanism Based on SOT and Rematching in Multiple Object Tracking
张毅锋 1张嘉成 1李元浩1
作者信息
- 1. 东南大学信息科学与工程学院,南京 211102
- 折叠
摘要
数据关联是多目标跟踪(Multiple object tracking,MOT)中的重要步骤,一般需要根据特征相似性实现目标和检测物体之间的身份匹配.部分目标或检测物体可能在匹配结束后仍处于孤立状态,可能导致轨迹中断或身份错乱的遗漏现象.为改善MOT的精度和稳定性,抑制数据关联中的遗漏现象,提出了一种基于高性能单目标跟踪器(Single object tracker,SOT)和重匹配的防遗漏机制.该机制运用Transformer和扩散模型,设计了一款契合MOT需求的SOT用于追踪遗漏目标,并通过记忆目标信息对遗漏检测物体实施重匹配.通过消融实验验证了SOT和重匹配方法在防遗漏机制中的作用,并在标准数据集上测试了该机制对MOT算法跟踪性能的影响.结果表明,各算法加入该机制后性能获得全面改善,该机制可有效抑制MOT中的遗漏现象.
Abstract
Data association is an important step in multiple object tracking(MOT),which generally requires identity matching between objects and detections based on feature similarity.Some objects or detections may remain isolated after match is completed,which is the missing phenomenon that may lead to track interruption or identity confusion.Therefore,in order to improve the accuracy and stability of MOT and suppress the missing phenomenon in data association,this paper proposes an anti-missing mechanism based on high-performance single object tracker(SOT)and rematching.The mechanism uses Transformer and diffusion model to design a SOT that meets the requirements of MOT to track missing objects and rematch missing detections by remembering the object information.The effect of SOT and rematching methods in anti-missing mechanism is verified by ablation experiments,and the effect of this mechanism on the tracking performance of MOT algorithm is tested on standard datasets.The results show that the performance of all algorithms is improved comprehensively with the addition of this mechanism,which can effectively suppress the missing phenomenon in MOT.
关键词
多目标跟踪/数据关联/遗漏现象/单目标跟踪器/重匹配Key words
multiple object tracking/data association/missing phenomenon/single object tracker/rematching引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据